Overview

Dataset statistics

Number of variables36
Number of observations194894
Missing cells904125
Missing cells (%)12.9%
Total size in memory53.5 MiB
Average record size in memory288.0 B

Variable types

Numeric23
Text11
DateTime1
Unsupported1

Alerts

cabin_reviews has 55920 (28.7%) missing valuesMissing
port_reviews has 188514 (96.7%) missing valuesMissing
r_Age has 7777 (4.0%) missing valuesMissing
s_price has 194894 (100.0%) missing valuesMissing
dining has 2807 (1.4%) missing valuesMissing
entertainment has 8166 (4.2%) missing valuesMissing
publicRooms has 4321 (2.2%) missing valuesMissing
fitnessAndRecreation has 40518 (20.8%) missing valuesMissing
family has 193805 (99.4%) missing valuesMissing
enrichmentActivities has 38758 (19.9%) missing valuesMissing
service has 6307 (3.2%) missing valuesMissing
valueForMoney has 13117 (6.7%) missing valuesMissing
embarkation has 2781 (1.4%) missing valuesMissing
onboardExperience has 99730 (51.2%) missing valuesMissing
shoreExcursions has 42388 (21.7%) missing valuesMissing
s_price is an unsupported type, check if it needs cleaning or further analysisUnsupported
r_Has_Children has 164202 (84.3%) zerosZeros
r_Helpful_Votes has 86012 (44.1%) zerosZeros
s_Professional_Rating has 5653 (2.9%) zerosZeros
s_Is_River has 181378 (93.1%) zerosZeros
s_Is_Luxury has 180132 (92.4%) zerosZeros
cabin has 2066 (1.1%) zerosZeros

Reproduction

Analysis started2024-05-09 13:46:47.344993
Analysis finished2024-05-09 13:49:12.302474
Duration2 minutes and 24.96 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

ShipId
Real number (ℝ)

Distinct689
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean465.1357148
Minimum4
Maximum1507
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-05-09T16:49:15.178063image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile74
Q1274
median381
Q3642
95-th percentile996
Maximum1507
Range1503
Interquartile range (IQR)368

Descriptive statistics

Standard deviation283.1498463
Coefficient of variation (CV)0.6087467319
Kurtosis0.3740797717
Mean465.1357148
Median Absolute Deviation (MAD)149
Skewness0.9027753867
Sum90652160
Variance80173.83544
MonotonicityNot monotonic
2024-05-09T16:49:15.242369image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
481 3970
 
2.0%
368 3666
 
1.9%
642 3651
 
1.9%
651 3017
 
1.5%
530 2995
 
1.5%
265 2852
 
1.5%
691 2807
 
1.4%
706 2739
 
1.4%
389 2574
 
1.3%
303 2528
 
1.3%
Other values (679) 164095
84.2%
ValueCountFrequency (%)
4 1042
0.5%
5 1067
0.5%
12 919
0.5%
13 934
0.5%
26 316
 
0.2%
ValueCountFrequency (%)
1507 6
< 0.1%
1486 7
< 0.1%
1483 7
< 0.1%
1482 6
< 0.1%
1457 1
 
< 0.1%
Distinct689
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2024-05-09T16:49:15.391852image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length42
Median length31
Mean length15.43232732
Min length4

Characters and Unicode

Total characters3007668
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)< 0.1%

Sample

1st row50 Years of Victory (Poseidon Expeditions)
2nd rowAdmiralty Dream
3rd rowAdmiralty Dream
4th rowAdmiralty Dream
5th rowAdmiralty Dream
ValueCountFrequency (%)
of 41331
 
8.9%
seas 39974
 
8.6%
the 39069
 
8.4%
norwegian 35610
 
7.7%
carnival 27231
 
5.9%
celebrity 17988
 
3.9%
princess 17520
 
3.8%
viking 12069
 
2.6%
msc 8264
 
1.8%
star 4969
 
1.1%
Other values (665) 219432
47.3%
2024-05-09T16:49:15.616236image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 329171
 
10.9%
a 279142
 
9.3%
268563
 
8.9%
i 238257
 
7.9%
r 211103
 
7.0%
n 189827
 
6.3%
o 140225
 
4.7%
s 125530
 
4.2%
t 124444
 
4.1%
l 110090
 
3.7%
Other values (53) 991316
33.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3007668
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 329171
 
10.9%
a 279142
 
9.3%
268563
 
8.9%
i 238257
 
7.9%
r 211103
 
7.0%
n 189827
 
6.3%
o 140225
 
4.7%
s 125530
 
4.2%
t 124444
 
4.1%
l 110090
 
3.7%
Other values (53) 991316
33.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3007668
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 329171
 
10.9%
a 279142
 
9.3%
268563
 
8.9%
i 238257
 
7.9%
r 211103
 
7.0%
n 189827
 
6.3%
o 140225
 
4.7%
s 125530
 
4.2%
t 124444
 
4.1%
l 110090
 
3.7%
Other values (53) 991316
33.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3007668
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 329171
 
10.9%
a 279142
 
9.3%
268563
 
8.9%
i 238257
 
7.9%
r 211103
 
7.0%
n 189827
 
6.3%
o 140225
 
4.7%
s 125530
 
4.2%
t 124444
 
4.1%
l 110090
 
3.7%
Other values (53) 991316
33.0%
Distinct187636
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2024-05-09T16:49:16.206307image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length34825
Median length20578
Mean length3463.414564
Min length1

Characters and Unicode

Total characters674998718
Distinct characters690
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique180443 ?
Unique (%)92.6%

Sample

1st rowJourney to the Arctic Circle , Was a adventure for my husband and I. Our ship held 125 people, but they were only about 100 People on the cruise. The ship was small and sturdy, compared to the huge cruise ships that are out now. Our cabin was nicely appointed, and their attention to detail was phenomenal. We felt very well taken care of. We had the most amazing chef. Every meal was an adventure in dining. I can honestly say I don’t think one person left hungry. And if they did it was their own fault.. I really can’t phantom how they could make each meal so unique and delicious.. We embarked in Edinburg Scotland,We were then went up the coast of Scotland, stopping to go on some expeditions. They were well thought out, and they hired local guides, and great busses. We went to Jan MayenIsland, There were no people living on the island. The island was stark in its beauty and one place I never ever thought that I would go. And it was on to Swalvard and Spitsbergen. We did a lot of hiking and our guides made sure that we were safe. Safe from the polar bears!, We will be sure to go On another Poseidon tour. There was a lot of value for the money and we were thrilled to make so many good friends.
2nd rowThis ship is in need of some time in dry dock! My cabin had no heat, had mold around the window, and the shower/toilet combo smelled like sewage. The ship’s generators both were broken for several hours one evening meaning no power. The lounge is shabby and tired. The zodiac engine quit one day and passengers had to be rowed back to the ship. And - the anchor cable broke! The website told me I would be on hikes with knowledgeable naturalists. The education is one of the reasons why I choose these small ship cruises. Well, IF you are able to participate with the strenuous group, you got the knowledgeable naturalists as a guide. On a moderate or a leisurely hike, you didn’t. On a leisurely hike we were led by kitchen staff. Nice people - but not naturalists. Through the week, only a couple of PA announcements about wildlife sightings. So, some of us missed out because we were not in the right place at the right time. All passengers were going on an evening hike and we were told to stay together and that the trail was just “straight up” the road. Within a short time, three of us slower hikers could see the group disappear as they followed the guide up the hill. When we realized we were lost - with no water, no map, no flashlight, no staff person sweeping - we retraced our steps and met the guide who was looking for us and acted as if we were to blame. There was a left turn on the trail and that had been omitted from the initial instructions. I was not frightened, but I was angry. Irresponsible and most unprofessional. The food was good, plentiful, with a varied menu. The wait staff was great. My fellow passengers were a good bunch. I had planned this trip for a couple of years, so the actual experience was most disappointing. Fortunately, my month-long Alaska adventure continued with spectacular experiences - professional in every way.
3rd rowI am new to expedition cruises, but I will be watching for them in the future! We did the inside passage in Alaska. And the difference in what a big cruiser experiences and what we experienced is amazing! We saw great wildlife in addition to amazing, majestic views. We could kayak off the back of the boat. We saw zillions of sea lions sunbathing on rocks. Otters floating by with babies. Black and brown bears. Many humpbacks, including 15 of them bubble net fishing. Sometimes I would look around the boat and realize that any of the four views around the boat could be in a national parks brochure. Seriously, breathtaking. The Admiralty Dream experienced some bumps, being an early sailing after the COVID 1 year break in cruising. The toilets didn't flush for a couple of days. The staff would be the bucket patrol, or hubby and I just filled the trash can up and poured it in the toilet when we needed to flush. They got the needed parts and a plumber and fixed it. Honestly, manually flushing toilets is not going to negatively impact my dream trip! :) We had 40 people onboard and we found the staff to be AMAZING. Friendly, accommodating, hard working. I couldn't ask for more. The rooms are small, not fancy. No music and dancing on a stage! The stage is natures work! We hiked on more than one island, kayaked, took rides in the zodiak. Toured up to the Canadian border (couldn't pass because the border was still closed). Naturalists on board (and on hikes), so lots of chances to learn about wildlife, birds, and the area. We saw so many glaciers, I lost track! I recommend. Heartily.
4th rowI have held off submitting this review to get a bit of perspective and to wait for the cruise line’s response to the unacceptable conditions under which we cruised on the Admiralty Dream ship in June. Alaskan Dream advertises a small ship experience with lots of wildlife, excellent food, native and Forest Service speakers and zodiac excursions. Unfortunately, the five night sailing we took left much to be desired, with the exception of the viewing of a large pod of humpback whales bubble feeding, which enthralled all the guests. The ship holds about 50 passengers. The cabins are small, with most having two single beds laid out in an L-shape and a bathroom with a “shoilet”, shower toilet combination. If you are tall, the beds will be too short for you. I was amazed to read a prior review that mentioned that there was an issue with the toilets, but not a big deal. Actually beginning the first night from embarking, the toilets did not flush, and, in some cases, overflowed onto the cabin decks. A number of cabins were uninhabitable due to unsanitary conditions, and, with their doors left open, the smell was very unpleasant as one walked by. Although the engineer tried to fix the plumbing, none of the toilets on the ship flushed for the duration of the trip, until the last evening heading to disembarkation. It was left to the restaurant servers, who doubled as cabin attendants, to pour water into the toilets to get them to flush. After a day of this, it was suggested that the passengers fill their wastebaskets and pour the water into the toilets to flush them, thus avoiding having a stranger have to do it. So, reluctantly, we did. It was not the most sanitary of conditions because the restaurant servers would clean the room and bathroom floors, and then, with the same gloves on, deliver clean towels and other cabin amenities. They then would serve meals. The passengers had concern about fecal contamination, norovirus and illness because of this. When finally on the last evening, a new part and engineer arrived to fix the plumbing, it was very welcome. But it was obvious that the ship had sat in drydock during COVID and just was not ready for passengers. We know this to be true because the passengers on the prior cruise, who were just disembarking as we were arriving, said the “toilets did not flush.” So the cruise line was aware of the problem and chose to sail anyway. As to food, it was a circus. If one did not arrive right on the set dining time for breakfast, only cereal was available. One morning the waitress announced, “we have 30 orders for omelets so be prepared to wait a long time.” The food, when it arrived, was consistently cold and overcooked. Eggs were cold, pancakes were cold, meat was cold, and hamburgers were cooked to burning well done. After the first lunch, it was announced, “we are out of hamburger buns so you will have to eat your burgers on Texas toast.” Since we had port visits, no one could figure out why they did not go to the store and buy some. We were told they were out of entrees, although printed on the menus we were given. No ice cream but vanilla, although different flavors were listed. The pork tenderloin was so overpeppered that it tasted as if the entire pepper shaker was emptied on the food. When our entire table sent it back, the problem was never conveyed to the chef or dining room chief. We all went away without dinner that evening. We were told to eat lunch while in Skagway because they did not want to have to cook lunch on the ship. The lunch would have been at our expense. After many complaints, they finally did, but it was cold, with no hamburger buns. We were supposed to have a gala dinner at the company’s private island, with a beach barbeque of Crab Legs, Prime Rib and Salmon. It was supposed to be mid-cruise, but they took us there the first night that we boarded, when people were tired from travel. No one was cooking when we arrived. The bar was a cash bar although drinks were included in the price of our room. After a few hours, we were called to a buffet to get our dinner, and eat outside on picnic tables. While it was a nice environment, there was no ambiance and frankly people ate and left. The Prime Rib was cooked to a brown black, overdone and tough. The Salmon was so overcooked, it was hard. The Crab legs were obviously frozen, rubbery and salty. Dessert was a piece of packaged cheesecake. No one came by to see if we were enjoying the dinner. There was no entertainment and it was a non-event. It felt like no one cared. The ship advertised “cookie time” in the afternoon, but we were quickly told that we were allowed one cookie each and that was it. Appetizer time turned out to be five broken tortilla chips and a tablespoon of salsa. The ship departed from Juneau and when we arrived at the airport, we were met and taken to our Airbnb, which we appreciated. We had no information on the location of the ship or when to meet. We were told we would be picked up at 10 am and taken down about 6 blocks to the wharf to the hospitality room. From there we would be driven to the Alaskan State Museum and Mendenhall Glacier for a visit before boarding. I called the shore person in charge of transportation, Lynn, and confirmed they had our address and would be picked up at 10 am. In the morning, we stood outside the apartment in the rain, and waited. At 10:30 we called and only got voicemail. We called another number and got voicemail. At 11 am we called a taxi and took it the six blocks to the wharf, paying $10. Lynn was standing there when we arrived and we asked where the transportation for us was. She said, “Oh, I forgot.” Well, we were not the only ones. People were left at the airport, at their hotels and not transported back to the airport in Sitka as promised. A BIG lack of communication. There were no locks on the cabin doors and no safe or place to put valuables or passports. One night at 3 am, in a cabin with two women passengers, the cabin door opened and a strange man walked into the room. They yelled and he left. But security was not high. As for COVID, all passengers had to be vaccinated to cruise. And, we had to provide a negative PCR test taken within 96 hours of boarding. And, we were sent a temperature chart and told we had to chart our temperature for 14 days before boarding. When we arrived, we offered the charts and were told, “Oh we don’t need those.” And, the vaccination certificate and PCR test were never acknowledged. We were told we needed to wear masks anytime we were indoors, in the lounge for example. But, after the first day, virtually no one was masked. And, when we entered the dining room, we were told our temperature needed to be taken. But, some people were missed and sat down anyway. And, often people’s temperatures read 92 degrees, which is obviously wrong. No one did anything. The cruise line advertises native American speakers and talks from the National Forest Service during the visit to Glacier Bay. No such speakers materialized and it was left to the two expedition leaders to make small talk. They did their best. The cruise line advertises zodiac trips to see wildlife and glaciers and scenery. We had one, short one. Kayaking did take place, but there were no safety measures to ensure that those who did kayak were capable of dealing with tides and waves. A number of people struggled to get back to the ship. The Captain was available and forthright about the plumbing issues, but had little control over the situation. He did his best when we spotted a pod of whales bubble feeding and kept the ship at that location for a number of hours. It was the highlight of the cruise. On the last day so many passengers were fed up with the conditions on the ship that 10 of us asked to meet with the Captain and Hotel Manager. They did listen to each person’s complaints and, the Captain called headquarters and asked that an Executive with Alaskan Dream come to the ship to address the passengers before we disembarked. Mr. Mingo came on the ship and told everyone that the situation was unacceptable and that we would be contacted about compensation. Unfortunately, we were sent an email asking us to review our cruise and nothing else. We heard nothing for weeks. So, a number of us contacted the new Reservations Manager who, it turned out had been assigned by Mr. Mingo to speak to us. She had been on the job one week. Despite a number of promised phone calls, emails and refunds, very few of the passengers ever received any compensation. People who had future cruises on the line cancelled them immediately. We did receive some compensation. But it will never make up for the long travel, expense of flying to Alaska, paying a premium price over most Alaska cruises and dashed hopes when promises are not kept. We truly wanted to love this cruise line as we do believe that small ship cruising is the way to go. But unfortunately, the family tradition of these small ships providing an exceptional experience to beautiful Alaska, was not fulfilled. As a sad ending to the cruise, we docked at the home of the Allens who founded the company, Allen Marine, and the whale watching boats and cruises. Mrs. Allen, wife of the founder was standing at her window, waving to us as we disembarked. I do not think she would be proud of what happened to us on the cruise.
5th rowThe Last Frontier Adventure cruise aboard the Admiralty Dream was fantastic from start to finish! This small-ship cruise line has figured out how to wow its passengers while providing opportunities on the Last Frontier Adventure cruise to view wild life (sea-going, air-going and land loving), to get up close and personal with glaciers, to learn about Tlingit history and culture and about the flora and fauna of Alaska. The staff is over-the-top service oriented, diverse, well-educated and fun to be around! They appear proud to be members of the crew, respectful of each other and the passengers and happy to be of service. Food is well-prepared and the menu always contains a range of offerings to appeal to every pallet, including special dietary needs such as vegetarian, vegan and gluten-free. Since all port visits and shore excursions are included in the cost of the cruise, once you are met at the airport by cruise line staff, you can relax and participate on the exciting on shore or on water experiences that will provide you treasured memories, ample photo opportunities and valuable educational experiences. The ship, Admiralty Dream, is small enough (we had approximately 50 passengers and 21 staff/crew aboard) that we could go into the more narrow passages that large cruise ships avoid, could get close to shore out in the wilds and could easily stop to enjoy wildlife viewing from the decks. Our cabin, while small, was well-lit, in excellent condition and working order and had comfortable beds. All in all the cruise was an "exceeded expectations" experience with only one caveat - be certain to book a cabin in the forward portion of the ship and away from air circulation vents to ensure a quieter ambiance in your cabin.
ValueCountFrequency (%)
the 7830369
 
6.3%
and 4072191
 
3.3%
to 3368347
 
2.7%
a 2852398
 
2.3%
was 2746448
 
2.2%
we 2728454
 
2.2%
of 2058249
 
1.7%
in 1695108
 
1.4%
on 1573702
 
1.3%
i 1461883
 
1.2%
Other values (624138) 93261442
75.4%
2024-05-09T16:49:16.650639image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
127530000
18.9%
e 66431085
 
9.8%
t 45950605
 
6.8%
a 43919166
 
6.5%
o 39687050
 
5.9%
n 34373642
 
5.1%
i 32814314
 
4.9%
r 31643429
 
4.7%
s 31525828
 
4.7%
h 25719691
 
3.8%
Other values (680) 195403908
28.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 674998718
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
127530000
18.9%
e 66431085
 
9.8%
t 45950605
 
6.8%
a 43919166
 
6.5%
o 39687050
 
5.9%
n 34373642
 
5.1%
i 32814314
 
4.9%
r 31643429
 
4.7%
s 31525828
 
4.7%
h 25719691
 
3.8%
Other values (680) 195403908
28.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 674998718
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
127530000
18.9%
e 66431085
 
9.8%
t 45950605
 
6.8%
a 43919166
 
6.5%
o 39687050
 
5.9%
n 34373642
 
5.1%
i 32814314
 
4.9%
r 31643429
 
4.7%
s 31525828
 
4.7%
h 25719691
 
3.8%
Other values (680) 195403908
28.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 674998718
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
127530000
18.9%
e 66431085
 
9.8%
t 45950605
 
6.8%
a 43919166
 
6.5%
o 39687050
 
5.9%
n 34373642
 
5.1%
i 32814314
 
4.9%
r 31643429
 
4.7%
s 31525828
 
4.7%
h 25719691
 
3.8%
Other values (680) 195403908
28.9%

cabin_reviews
Text

MISSING 

Distinct130446
Distinct (%)93.9%
Missing55920
Missing (%)28.7%
Memory size1.5 MiB
2024-05-09T16:49:16.843363image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length19777
Median length3462
Mean length265.1385007
Min length1

Characters and Unicode

Total characters36847358
Distinct characters168
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique124676 ?
Unique (%)89.7%

Sample

1st rowVery clean and well-maintained
2nd rowLocation good on the main deck, easy access to dining and the lounge. And being an inside cabin, my window looked directly to the water. Upper decks have cabin windows looking onto the walkway around the ship so privacy is an issue. One night I sat on my bed looking out the window and watching bio-fluorescence - very cool. But my cabin was good for a single, very cozy for a couple.
3rd rowSmall, not fancy, but functional. Cabin doors don't lock.
4th rowlocated in the aft of the ship - so noisier than cabins located more forward in the ship; cabin was in excellent condition and beds are comfortable
5th rowAdequate but over galley and constantly smelled of grease; not good when they just made you sick.
ValueCountFrequency (%)
the 428200
 
6.4%
and 223471
 
3.3%
was 200130
 
3.0%
a 170549
 
2.5%
to 136547
 
2.0%
of 117670
 
1.8%
we 103829
 
1.6%
cabin 103248
 
1.5%
in 87335
 
1.3%
room 80661
 
1.2%
Other values (78325) 5046990
75.3%
2024-05-09T16:49:17.102755image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6670190
18.1%
e 3469061
 
9.4%
a 2486232
 
6.7%
o 2385840
 
6.5%
t 2385627
 
6.5%
n 1816371
 
4.9%
i 1706119
 
4.6%
s 1693528
 
4.6%
r 1676384
 
4.5%
h 1349625
 
3.7%
Other values (158) 11208381
30.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36847358
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6670190
18.1%
e 3469061
 
9.4%
a 2486232
 
6.7%
o 2385840
 
6.5%
t 2385627
 
6.5%
n 1816371
 
4.9%
i 1706119
 
4.6%
s 1693528
 
4.6%
r 1676384
 
4.5%
h 1349625
 
3.7%
Other values (158) 11208381
30.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36847358
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6670190
18.1%
e 3469061
 
9.4%
a 2486232
 
6.7%
o 2385840
 
6.5%
t 2385627
 
6.5%
n 1816371
 
4.9%
i 1706119
 
4.6%
s 1693528
 
4.6%
r 1676384
 
4.5%
h 1349625
 
3.7%
Other values (158) 11208381
30.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36847358
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6670190
18.1%
e 3469061
 
9.4%
a 2486232
 
6.7%
o 2385840
 
6.5%
t 2385627
 
6.5%
n 1816371
 
4.9%
i 1706119
 
4.6%
s 1693528
 
4.6%
r 1676384
 
4.5%
h 1349625
 
3.7%
Other values (158) 11208381
30.4%

port_reviews
Text

MISSING 

Distinct5500
Distinct (%)86.2%
Missing188514
Missing (%)96.7%
Memory size1.5 MiB
2024-05-09T16:49:17.259772image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length5922
Median length1447
Mean length365.9916928
Min length1

Characters and Unicode

Total characters2335027
Distinct characters128
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5211 ?
Unique (%)81.7%

Sample

1st row">
2nd rowthe bus to the beach was horrible and the ac was leaking on my grandmother head.
3rd rowJust average
4th rowWe spent $7 for shuttle to Sandcastle Resort. Our $5 entrance fee got us use of the resort pool, bathrooms, outdoor shower, chairs, and umbrellas. We loved this resort and beach- calm water, clean beach, not over crowded, great to relax and snorkel. No locals selling jewelry. Resort bar and café had great drinks and food, reasonably priced. The shuttle price was also for return trip and they ran every 20mins. My son did the certified 2 tank scuba dive and said it was terrific! He ould highly recommend it.
5th rowThere were good views of the island and talks about the people and their habits. Several stops but mostly wanting you to buy something. We were told that 65% of their population works with tourists so tourists buying are important to them.
ValueCountFrequency (%)
the 27732
 
6.4%
and 15396
 
3.5%
to 13618
 
3.1%
a 12201
 
2.8%
we 10150
 
2.3%
was 9906
 
2.3%
of 7668
 
1.8%
it 5129
 
1.2%
in 4704
 
1.1%
on 4320
 
1.0%
Other values (17357) 324627
74.5%
2024-05-09T16:49:17.479046image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
435916
18.7%
e 221050
 
9.5%
t 164616
 
7.0%
a 152525
 
6.5%
o 142531
 
6.1%
n 113489
 
4.9%
r 109466
 
4.7%
i 108540
 
4.6%
s 103147
 
4.4%
h 91101
 
3.9%
Other values (118) 692646
29.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2335027
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
435916
18.7%
e 221050
 
9.5%
t 164616
 
7.0%
a 152525
 
6.5%
o 142531
 
6.1%
n 113489
 
4.9%
r 109466
 
4.7%
i 108540
 
4.6%
s 103147
 
4.4%
h 91101
 
3.9%
Other values (118) 692646
29.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2335027
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
435916
18.7%
e 221050
 
9.5%
t 164616
 
7.0%
a 152525
 
6.5%
o 142531
 
6.1%
n 113489
 
4.9%
r 109466
 
4.7%
i 108540
 
4.6%
s 103147
 
4.4%
h 91101
 
3.9%
Other values (118) 692646
29.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2335027
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
435916
18.7%
e 221050
 
9.5%
t 164616
 
7.0%
a 152525
 
6.5%
o 142531
 
6.1%
n 113489
 
4.9%
r 109466
 
4.7%
i 108540
 
4.6%
s 103147
 
4.4%
h 91101
 
3.9%
Other values (118) 692646
29.7%
Distinct46
Distinct (%)< 0.1%
Missing51
Missing (%)< 0.1%
Memory size1.5 MiB
2024-05-09T16:49:17.589310image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length21
Median length18
Mean length13.02378325
Min length3

Characters and Unicode

Total characters2537593
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowarctic
2nd rowalaska
3rd rowalaska
4th rowalaska
5th rowalaska
ValueCountFrequency (%)
western-caribbean 31891
16.4%
eastern-caribbean 26556
13.6%
bahamas 13111
 
6.7%
southern-caribbean 11965
 
6.1%
europe-river 10100
 
5.2%
alaska 9295
 
4.8%
caribbean 7922
 
4.1%
western-mediterranean 7746
 
4.0%
baltics 6998
 
3.6%
mediterranean 6780
 
3.5%
Other values (36) 62479
32.1%
2024-05-09T16:49:18.537426image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 416499
16.4%
e 362829
14.3%
r 278841
11.0%
n 237865
9.4%
b 186784
7.4%
i 178025
7.0%
s 148079
 
5.8%
t 143051
 
5.6%
c 120366
 
4.7%
- 118794
 
4.7%
Other values (15) 346460
13.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2537593
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 416499
16.4%
e 362829
14.3%
r 278841
11.0%
n 237865
9.4%
b 186784
7.4%
i 178025
7.0%
s 148079
 
5.8%
t 143051
 
5.6%
c 120366
 
4.7%
- 118794
 
4.7%
Other values (15) 346460
13.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2537593
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 416499
16.4%
e 362829
14.3%
r 278841
11.0%
n 237865
9.4%
b 186784
7.4%
i 178025
7.0%
s 148079
 
5.8%
t 143051
 
5.6%
c 120366
 
4.7%
- 118794
 
4.7%
Other values (15) 346460
13.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2537593
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 416499
16.4%
e 362829
14.3%
r 278841
11.0%
n 237865
9.4%
b 186784
7.4%
i 178025
7.0%
s 148079
 
5.8%
t 143051
 
5.6%
c 120366
 
4.7%
- 118794
 
4.7%
Other values (15) 346460
13.7%
Distinct2570
Distinct (%)1.3%
Missing60
Missing (%)< 0.1%
Memory size1.5 MiB
Minimum2000-01-01 00:00:00
Maximum2021-11-30 00:00:00
2024-05-09T16:49:18.609333image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-09T16:49:18.672013image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct150390
Distinct (%)77.2%
Missing106
Missing (%)0.1%
Memory size1.5 MiB
2024-05-09T16:49:18.830711image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length138
Median length92
Mean length30.99171407
Min length1

Characters and Unicode

Total characters6036814
Distinct characters150
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique137370 ?
Unique (%)70.5%

Sample

1st rowFun and adventures on the high sea, Journey to the Arctic Circle
2nd rowDisappointing experience
3rd rowInside Passage on an expedition cruise
4th rowA VERY DISAPPOINTING, UNSATISFACTORY ALASKAN DREAM CRUISE
5th rowWelcome to the Wilds of Alaska!
ValueCountFrequency (%)
cruise 47062
 
4.7%
the 38986
 
3.9%
24234
 
2.4%
a 23123
 
2.3%
great 22138
 
2.2%
and 18268
 
1.8%
of 17974
 
1.8%
on 17223
 
1.7%
to 16417
 
1.6%
ship 15635
 
1.5%
Other values (25928) 769041
76.1%
2024-05-09T16:49:19.077153image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
822734
 
13.6%
e 581652
 
9.6%
i 394195
 
6.5%
a 391369
 
6.5%
r 358309
 
5.9%
t 347475
 
5.8%
n 311319
 
5.2%
o 290423
 
4.8%
s 284637
 
4.7%
l 166123
 
2.8%
Other values (140) 2088578
34.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6036814
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
822734
 
13.6%
e 581652
 
9.6%
i 394195
 
6.5%
a 391369
 
6.5%
r 358309
 
5.9%
t 347475
 
5.8%
n 311319
 
5.2%
o 290423
 
4.8%
s 284637
 
4.7%
l 166123
 
2.8%
Other values (140) 2088578
34.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6036814
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
822734
 
13.6%
e 581652
 
9.6%
i 394195
 
6.5%
a 391369
 
6.5%
r 358309
 
5.9%
t 347475
 
5.8%
n 311319
 
5.2%
o 290423
 
4.8%
s 284637
 
4.7%
l 166123
 
2.8%
Other values (140) 2088578
34.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6036814
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
822734
 
13.6%
e 581652
 
9.6%
i 394195
 
6.5%
a 391369
 
6.5%
r 358309
 
5.9%
t 347475
 
5.8%
n 311319
 
5.2%
o 290423
 
4.8%
s 284637
 
4.7%
l 166123
 
2.8%
Other values (140) 2088578
34.6%
Distinct144991
Distinct (%)74.4%
Missing51
Missing (%)< 0.1%
Memory size1.5 MiB
2024-05-09T16:49:19.263303image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length31
Median length27
Mean length9.516092444
Min length1

Characters and Unicode

Total characters1854144
Distinct characters101
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique118522 ?
Unique (%)60.8%

Sample

1st rowOnesavvyone
2nd rowWhereToNext5050
3rd rowHeart Vacations
4th rowharbormaster
5th rowAlaskan Dreamer
ValueCountFrequency (%)
cruiser 892
 
0.4%
the 627
 
0.3%
cruise 536
 
0.2%
and 439
 
0.2%
cruising 298
 
0.1%
girl 229
 
0.1%
from 224
 
0.1%
cruisers 220
 
0.1%
a 218
 
0.1%
traveler 209
 
0.1%
Other values (139374) 215675
98.2%
2024-05-09T16:49:19.502055image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 159430
 
8.6%
a 146320
 
7.9%
r 124717
 
6.7%
i 108676
 
5.9%
n 100649
 
5.4%
o 94281
 
5.1%
s 85962
 
4.6%
l 85577
 
4.6%
t 69110
 
3.7%
c 54611
 
2.9%
Other values (91) 824811
44.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1854144
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 159430
 
8.6%
a 146320
 
7.9%
r 124717
 
6.7%
i 108676
 
5.9%
n 100649
 
5.4%
o 94281
 
5.1%
s 85962
 
4.6%
l 85577
 
4.6%
t 69110
 
3.7%
c 54611
 
2.9%
Other values (91) 824811
44.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1854144
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 159430
 
8.6%
a 146320
 
7.9%
r 124717
 
6.7%
i 108676
 
5.9%
n 100649
 
5.4%
o 94281
 
5.1%
s 85962
 
4.6%
l 85577
 
4.6%
t 69110
 
3.7%
c 54611
 
2.9%
Other values (91) 824811
44.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1854144
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 159430
 
8.6%
a 146320
 
7.9%
r 124717
 
6.7%
i 108676
 
5.9%
n 100649
 
5.4%
o 94281
 
5.1%
s 85962
 
4.6%
l 85577
 
4.6%
t 69110
 
3.7%
c 54611
 
2.9%
Other values (91) 824811
44.5%

r_User_Id
Real number (ℝ)

Distinct143956
Distinct (%)73.9%
Missing51
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1194063.195
Minimum1
Maximum4289243
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-05-09T16:49:19.580998image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile97821
Q1520755
median1134775
Q31528030.5
95-th percentile3529594.9
Maximum4289243
Range4289242
Interquartile range (IQR)1007275.5

Descriptive statistics

Standard deviation915219.5656
Coefficient of variation (CV)0.7664749816
Kurtosis2.168762831
Mean1194063.195
Median Absolute Deviation (MAD)468972
Skewness1.428849631
Sum2.326548552 × 1011
Variance8.376268533 × 1011
MonotonicityNot monotonic
2024-05-09T16:49:19.641636image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
649782 36
 
< 0.1%
497468 34
 
< 0.1%
336202 32
 
< 0.1%
126723 32
 
< 0.1%
373396 31
 
< 0.1%
625221 31
 
< 0.1%
326146 31
 
< 0.1%
50181 31
 
< 0.1%
116946 30
 
< 0.1%
1268528 28
 
< 0.1%
Other values (143946) 194527
99.8%
(Missing) 51
 
< 0.1%
ValueCountFrequency (%)
1 2
 
< 0.1%
36 6
< 0.1%
49 1
 
< 0.1%
54 1
 
< 0.1%
92 4
< 0.1%
ValueCountFrequency (%)
4289243 1
< 0.1%
4289189 1
< 0.1%
4289010 1
< 0.1%
4288990 1
< 0.1%
4288979 1
< 0.1%

r_Age
Real number (ℝ)

MISSING 

Distinct13
Distinct (%)< 0.1%
Missing7777
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean73.66668983
Minimum0
Maximum2020
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-05-09T16:49:19.691602image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q150
median60
Q370
95-th percentile70
Maximum2020
Range2020
Interquartile range (IQR)20

Descriptive statistics

Standard deviation181.3859917
Coefficient of variation (CV)2.462252507
Kurtosis110.577431
Mean73.66668983
Median Absolute Deviation (MAD)10
Skewness10.58206937
Sum13784290
Variance32900.87798
MonotonicityNot monotonic
2024-05-09T16:49:19.742200image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
60 55543
28.5%
70 47139
24.2%
50 42204
21.7%
40 21212
 
10.9%
30 9485
 
4.9%
80 7207
 
3.7%
20 2086
 
1.1%
2020 1603
 
0.8%
90 358
 
0.2%
110 120
 
0.1%
Other values (3) 160
 
0.1%
(Missing) 7777
 
4.0%
ValueCountFrequency (%)
0 1
 
< 0.1%
10 99
 
0.1%
20 2086
 
1.1%
30 9485
4.9%
40 21212
10.9%
ValueCountFrequency (%)
2020 1603
 
0.8%
110 120
 
0.1%
100 60
 
< 0.1%
90 358
 
0.2%
80 7207
3.7%

r_Overall_Rating
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing67
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3.905387857
Minimum0
Maximum5
Zeros1758
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-05-09T16:49:19.790016image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.259481027
Coefficient of variation (CV)0.3224983211
Kurtosis0.1287597806
Mean3.905387857
Median Absolute Deviation (MAD)1
Skewness-1.010903734
Sum760875
Variance1.586292458
MonotonicityNot monotonic
2024-05-09T16:49:19.832219image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 86546
44.4%
4 47174
24.2%
3 31240
 
16.0%
2 17620
 
9.0%
1 10489
 
5.4%
0 1758
 
0.9%
(Missing) 67
 
< 0.1%
ValueCountFrequency (%)
0 1758
 
0.9%
1 10489
 
5.4%
2 17620
 
9.0%
3 31240
16.0%
4 47174
24.2%
ValueCountFrequency (%)
5 86546
44.4%
4 47174
24.2%
3 31240
 
16.0%
2 17620
 
9.0%
1 10489
 
5.4%

r_Has_Children
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing1092
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean0.1527332019
Minimum0
Maximum1
Zeros164202
Zeros (%)84.3%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-05-09T16:49:19.872024image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3597310644
Coefficient of variation (CV)2.355290532
Kurtosis1.727706168
Mean0.1527332019
Median Absolute Deviation (MAD)0
Skewness1.930722232
Sum29600
Variance0.1294064387
MonotonicityNot monotonic
2024-05-09T16:49:19.925744image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 164202
84.3%
1 29600
 
15.2%
(Missing) 1092
 
0.6%
ValueCountFrequency (%)
0 164202
84.3%
1 29600
 
15.2%
ValueCountFrequency (%)
1 29600
 
15.2%
0 164202
84.3%
Distinct4
Distinct (%)< 0.1%
Missing56
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2.519898582
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-05-09T16:49:19.972023image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q34
95-th percentile4
Maximum4
Range3
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.148982916
Coefficient of variation (CV)0.4559639519
Kurtosis-1.431952021
Mean2.519898582
Median Absolute Deviation (MAD)1
Skewness0.02420360623
Sum490972
Variance1.320161741
MonotonicityNot monotonic
2024-05-09T16:49:20.013899image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
4 55736
28.6%
2 52166
26.8%
1 48556
24.9%
3 38380
19.7%
(Missing) 56
 
< 0.1%
ValueCountFrequency (%)
1 48556
24.9%
2 52166
26.8%
3 38380
19.7%
4 55736
28.6%
ValueCountFrequency (%)
4 55736
28.6%
3 38380
19.7%
2 52166
26.8%
1 48556
24.9%

r_Helpful_Votes
Real number (ℝ)

ZEROS 

Distinct160
Distinct (%)0.1%
Missing56
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3.025729067
Minimum0
Maximum671
Zeros86012
Zeros (%)44.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-05-09T16:49:20.065260image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile13
Maximum671
Range671
Interquartile range (IQR)4

Descriptive statistics

Standard deviation6.692117784
Coefficient of variation (CV)2.211737282
Kurtosis672.0010094
Mean3.025729067
Median Absolute Deviation (MAD)1
Skewness13.47566735
Sum589527
Variance44.78444043
MonotonicityNot monotonic
2024-05-09T16:49:20.127085image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 86012
44.1%
1 28281
 
14.5%
2 18209
 
9.3%
3 12933
 
6.6%
4 9698
 
5.0%
5 7296
 
3.7%
6 5720
 
2.9%
7 4396
 
2.3%
8 3600
 
1.8%
9 2869
 
1.5%
Other values (150) 15824
 
8.1%
ValueCountFrequency (%)
0 86012
44.1%
1 28281
 
14.5%
2 18209
 
9.3%
3 12933
 
6.6%
4 9698
 
5.0%
ValueCountFrequency (%)
671 1
< 0.1%
313 1
< 0.1%
277 1
< 0.1%
260 1
< 0.1%
248 1
< 0.1%

s_Average_Member_Rating
Real number (ℝ)

Distinct426
Distinct (%)0.2%
Missing62
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3.898512325
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-05-09T16:49:20.188889image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.42938
Q13.71153
median3.87745
Q34.08416
95-th percentile4.61049
Maximum5
Range4
Interquartile range (IQR)0.37263

Descriptive statistics

Standard deviation0.3331053443
Coefficient of variation (CV)0.08544421987
Kurtosis1.40669337
Mean3.898512325
Median Absolute Deviation (MAD)0.1812
Skewness0.2612047073
Sum759554.9534
Variance0.1109591704
MonotonicityNot monotonic
2024-05-09T16:49:20.251658image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.51622 3970
 
2.0%
4.09828 3666
 
1.9%
3.46251 3647
 
1.9%
3.64554 3015
 
1.5%
4.17938 2995
 
1.5%
3.72349 2851
 
1.5%
3.77074 2806
 
1.4%
3.66826 2739
 
1.4%
4.01328 2573
 
1.3%
3.87745 2528
 
1.3%
Other values (416) 164042
84.2%
ValueCountFrequency (%)
1 3
 
< 0.1%
1.42857 14
< 0.1%
2 12
< 0.1%
2.125 8
 
< 0.1%
2.16 25
< 0.1%
ValueCountFrequency (%)
5 353
0.2%
4.94444 18
 
< 0.1%
4.92857 44
 
< 0.1%
4.91667 37
 
< 0.1%
4.90667 75
 
< 0.1%

s_Professional_Rating
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing56
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean4.124159558
Minimum0
Maximum5
Zeros5653
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-05-09T16:49:20.301982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.5
Q14
median4
Q34.5
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.8142686843
Coefficient of variation (CV)0.1974386958
Kurtosis16.29049802
Mean4.124159558
Median Absolute Deviation (MAD)0.5
Skewness-3.644148967
Sum803543
Variance0.6630334902
MonotonicityNot monotonic
2024-05-09T16:49:20.343954image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 87059
44.7%
4.5 66682
34.2%
5 20991
 
10.8%
3.5 13869
 
7.1%
0 5653
 
2.9%
3 563
 
0.3%
2.5 21
 
< 0.1%
(Missing) 56
 
< 0.1%
ValueCountFrequency (%)
0 5653
 
2.9%
2.5 21
 
< 0.1%
3 563
 
0.3%
3.5 13869
 
7.1%
4 87059
44.7%
ValueCountFrequency (%)
5 20991
 
10.8%
4.5 66682
34.2%
4 87059
44.7%
3.5 13869
 
7.1%
3 563
 
0.3%

s_Is_River
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06935051874
Minimum0
Maximum1
Zeros181378
Zeros (%)93.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-05-09T16:49:20.383731image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2540499074
Coefficient of variation (CV)3.663273354
Kurtosis9.49429558
Mean0.06935051874
Median Absolute Deviation (MAD)0
Skewness3.390309448
Sum13516
Variance0.06454135545
MonotonicityNot monotonic
2024-05-09T16:49:20.426977image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 181378
93.1%
1 13516
 
6.9%
ValueCountFrequency (%)
0 181378
93.1%
1 13516
 
6.9%
ValueCountFrequency (%)
1 13516
 
6.9%
0 181378
93.1%

s_Is_Luxury
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing56
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.07547808949
Minimum0
Maximum1
Zeros180132
Zeros (%)92.4%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-05-09T16:49:20.468645image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2641618929
Coefficient of variation (CV)3.499848693
Kurtosis8.33076272
Mean0.07547808949
Median Absolute Deviation (MAD)0
Skewness3.214137086
Sum14706
Variance0.06978150565
MonotonicityNot monotonic
2024-05-09T16:49:20.512195image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 180132
92.4%
1 14706
 
7.5%
(Missing) 56
 
< 0.1%
ValueCountFrequency (%)
0 180132
92.4%
1 14706
 
7.5%
ValueCountFrequency (%)
1 14706
 
7.5%
0 180132
92.4%
Distinct115
Distinct (%)0.1%
Missing56
Missing (%)< 0.1%
Memory size1.5 MiB
2024-05-09T16:49:20.588134image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length66
Median length34
Mean length20.32901693
Min length3

Characters and Unicode

Total characters3960865
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowPoseidon Expeditions
2nd rowAlaskan Dream Cruises
3rd rowAlaskan Dream Cruises
4th rowAlaskan Dream Cruises
5th rowAlaskan Dream Cruises
ValueCountFrequency (%)
line 75118
14.5%
cruises 71120
13.7%
cruise 69991
13.5%
international 38833
7.5%
royal 38831
7.5%
caribbean 38831
7.5%
norwegian 37835
7.3%
carnival 27224
 
5.2%
celebrity 17982
 
3.5%
princess 17385
 
3.3%
Other values (157) 86012
16.6%
2024-05-09T16:49:20.737144image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 449026
11.3%
e 434205
11.0%
n 356659
 
9.0%
r 351450
 
8.9%
a 337314
 
8.5%
324324
 
8.2%
s 261937
 
6.6%
C 240024
 
6.1%
u 150416
 
3.8%
l 144666
 
3.7%
Other values (46) 910844
23.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3960865
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 449026
11.3%
e 434205
11.0%
n 356659
 
9.0%
r 351450
 
8.9%
a 337314
 
8.5%
324324
 
8.2%
s 261937
 
6.6%
C 240024
 
6.1%
u 150416
 
3.8%
l 144666
 
3.7%
Other values (46) 910844
23.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3960865
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 449026
11.3%
e 434205
11.0%
n 356659
 
9.0%
r 351450
 
8.9%
a 337314
 
8.5%
324324
 
8.2%
s 261937
 
6.6%
C 240024
 
6.1%
u 150416
 
3.8%
l 144666
 
3.7%
Other values (46) 910844
23.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3960865
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 449026
11.3%
e 434205
11.0%
n 356659
 
9.0%
r 351450
 
8.9%
a 337314
 
8.5%
324324
 
8.2%
s 261937
 
6.6%
C 240024
 
6.1%
u 150416
 
3.8%
l 144666
 
3.7%
Other values (46) 910844
23.0%
Distinct289
Distinct (%)0.1%
Missing176
Missing (%)0.1%
Memory size1.5 MiB
2024-05-09T16:49:20.896849image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length32
Median length5
Mean length4.927952218
Min length1

Characters and Unicode

Total characters959561
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st row128
2nd row58
3rd row58
4th row58
5th row58
ValueCountFrequency (%)
4,028 6662
 
3.3%
3,080 6244
 
3.1%
2,850 6110
 
3.0%
190 5741
 
2.9%
2,124 5279
 
2.6%
930 4971
 
2.5%
2,394 4793
 
2.4%
4,100 3970
 
2.0%
4,180 3889
 
1.9%
3,560 3793
 
1.9%
Other values (288) 149929
74.5%
2024-05-09T16:49:21.120933image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 162548
16.9%
0 135966
14.2%
2 133931
14.0%
4 88548
9.2%
1 79269
8.3%
3 69481
7.2%
9 61564
 
6.4%
6 56506
 
5.9%
8 53606
 
5.6%
5 52057
 
5.4%
Other values (27) 66085
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 959561
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 162548
16.9%
0 135966
14.2%
2 133931
14.0%
4 88548
9.2%
1 79269
8.3%
3 69481
7.2%
9 61564
 
6.4%
6 56506
 
5.9%
8 53606
 
5.6%
5 52057
 
5.4%
Other values (27) 66085
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 959561
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 162548
16.9%
0 135966
14.2%
2 133931
14.0%
4 88548
9.2%
1 79269
8.3%
3 69481
7.2%
9 61564
 
6.4%
6 56506
 
5.9%
8 53606
 
5.6%
5 52057
 
5.4%
Other values (27) 66085
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 959561
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 162548
16.9%
0 135966
14.2%
2 133931
14.0%
4 88548
9.2%
1 79269
8.3%
3 69481
7.2%
9 61564
 
6.4%
6 56506
 
5.9%
8 53606
 
5.6%
5 52057
 
5.4%
Other values (27) 66085
6.9%
Distinct245
Distinct (%)0.1%
Missing1600
Missing (%)0.8%
Memory size1.5 MiB
2024-05-09T16:49:21.276371image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length60
Median length5
Mean length4.162969363
Min length1

Characters and Unicode

Total characters804677
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row140
2nd row21
3rd row21
4th row21
5th row21
ValueCountFrequency (%)
1,200 11477
 
5.9%
1,100 11402
 
5.9%
1,250 6817
 
3.5%
1,595 6662
 
3.4%
1,500 5823
 
3.0%
550 4914
 
2.5%
1,160 4516
 
2.3%
1,360 4194
 
2.2%
1,185 4063
 
2.1%
1,738 3970
 
2.0%
Other values (238) 130256
67.1%
2024-05-09T16:49:21.487916image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 158138
19.7%
1 154752
19.2%
, 114412
14.2%
5 68793
8.5%
2 54802
 
6.8%
8 52941
 
6.6%
9 46707
 
5.8%
3 45438
 
5.6%
4 43025
 
5.3%
6 33560
 
4.2%
Other values (29) 32109
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 804677
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 158138
19.7%
1 154752
19.2%
, 114412
14.2%
5 68793
8.5%
2 54802
 
6.8%
8 52941
 
6.6%
9 46707
 
5.8%
3 45438
 
5.6%
4 43025
 
5.3%
6 33560
 
4.2%
Other values (29) 32109
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 804677
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 158138
19.7%
1 154752
19.2%
, 114412
14.2%
5 68793
8.5%
2 54802
 
6.8%
8 52941
 
6.6%
9 46707
 
5.8%
3 45438
 
5.6%
4 43025
 
5.3%
6 33560
 
4.2%
Other values (29) 32109
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 804677
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 158138
19.7%
1 154752
19.2%
, 114412
14.2%
5 68793
8.5%
2 54802
 
6.8%
8 52941
 
6.6%
9 46707
 
5.8%
3 45438
 
5.6%
4 43025
 
5.3%
6 33560
 
4.2%
Other values (29) 32109
 
4.0%
Distinct147
Distinct (%)0.1%
Missing383
Missing (%)0.2%
Memory size1.5 MiB
2024-05-09T16:49:21.614107image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length36
Median length4
Mean length4.926492589
Min length4

Characters and Unicode

Total characters958257
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)< 0.1%

Sample

1st row2007
2nd row1979, relaunched in 2011
3rd row1979, relaunched in 2011
4th row1979, relaunched in 2011
5th row1979, relaunched in 2011
ValueCountFrequency (%)
2001 14376
 
6.6%
2002 13945
 
6.4%
2007 13088
 
6.0%
2010 11845
 
5.5%
2015 11346
 
5.2%
2004 10479
 
4.8%
2008 10077
 
4.6%
2003 9332
 
4.3%
2009 9274
 
4.3%
2014 8993
 
4.1%
Other values (107) 104022
48.0%
2024-05-09T16:49:21.799311image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 294771
30.8%
2 196991
20.6%
1 118405
12.4%
9 60818
 
6.3%
8 23074
 
2.4%
7 22386
 
2.3%
22297
 
2.3%
r 20162
 
2.1%
4 19956
 
2.1%
3 19624
 
2.0%
Other values (41) 159773
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 958257
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 294771
30.8%
2 196991
20.6%
1 118405
12.4%
9 60818
 
6.3%
8 23074
 
2.4%
7 22386
 
2.3%
22297
 
2.3%
r 20162
 
2.1%
4 19956
 
2.1%
3 19624
 
2.0%
Other values (41) 159773
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 958257
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 294771
30.8%
2 196991
20.6%
1 118405
12.4%
9 60818
 
6.3%
8 23074
 
2.4%
7 22386
 
2.3%
22297
 
2.3%
r 20162
 
2.1%
4 19956
 
2.1%
3 19624
 
2.0%
Other values (41) 159773
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 958257
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 294771
30.8%
2 196991
20.6%
1 118405
12.4%
9 60818
 
6.3%
8 23074
 
2.4%
7 22386
 
2.3%
22297
 
2.3%
r 20162
 
2.1%
4 19956
 
2.1%
3 19624
 
2.0%
Other values (41) 159773
16.7%

s_price
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing194894
Missing (%)100.0%
Memory size1.5 MiB

cabin
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing343
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean4.169724134
Minimum0
Maximum6
Zeros2066
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-05-09T16:49:21.862130image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median5
Q35
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.107159266
Coefficient of variation (CV)0.2655233848
Kurtosis2.02319167
Mean4.169724134
Median Absolute Deviation (MAD)0
Skewness-1.494955207
Sum811224
Variance1.22580164
MonotonicityNot monotonic
2024-05-09T16:49:21.903608image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5 100133
51.4%
4 52146
26.8%
3 25773
 
13.2%
2 8638
 
4.4%
1 5478
 
2.8%
0 2066
 
1.1%
6 317
 
0.2%
(Missing) 343
 
0.2%
ValueCountFrequency (%)
0 2066
 
1.1%
1 5478
 
2.8%
2 8638
 
4.4%
3 25773
13.2%
4 52146
26.8%
ValueCountFrequency (%)
6 317
 
0.2%
5 100133
51.4%
4 52146
26.8%
3 25773
 
13.2%
2 8638
 
4.4%

dining
Real number (ℝ)

MISSING 

Distinct6
Distinct (%)< 0.1%
Missing2807
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean3.926288609
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-05-09T16:49:21.945179image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q35
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.207131595
Coefficient of variation (CV)0.3074485132
Kurtosis-0.1290056194
Mean3.926288609
Median Absolute Deviation (MAD)1
Skewness-0.9315724994
Sum754189
Variance1.457166688
MonotonicityNot monotonic
2024-05-09T16:49:21.987495image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 83747
43.0%
4 48628
25.0%
3 32300
 
16.6%
2 16295
 
8.4%
1 11050
 
5.7%
6 67
 
< 0.1%
(Missing) 2807
 
1.4%
ValueCountFrequency (%)
1 11050
 
5.7%
2 16295
 
8.4%
3 32300
 
16.6%
4 48628
25.0%
5 83747
43.0%
ValueCountFrequency (%)
6 67
 
< 0.1%
5 83747
43.0%
4 48628
25.0%
3 32300
 
16.6%
2 16295
 
8.4%

entertainment
Real number (ℝ)

MISSING 

Distinct6
Distinct (%)< 0.1%
Missing8166
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean3.803666295
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-05-09T16:49:22.027496image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q35
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.166012394
Coefficient of variation (CV)0.3065496032
Kurtosis-0.3068659879
Mean3.803666295
Median Absolute Deviation (MAD)1
Skewness-0.7391594605
Sum710251
Variance1.359584902
MonotonicityNot monotonic
2024-05-09T16:49:22.071071image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 66084
33.9%
4 53985
27.7%
3 39967
20.5%
2 16978
 
8.7%
1 9650
 
5.0%
6 64
 
< 0.1%
(Missing) 8166
 
4.2%
ValueCountFrequency (%)
1 9650
 
5.0%
2 16978
 
8.7%
3 39967
20.5%
4 53985
27.7%
5 66084
33.9%
ValueCountFrequency (%)
6 64
 
< 0.1%
5 66084
33.9%
4 53985
27.7%
3 39967
20.5%
2 16978
 
8.7%

publicRooms
Real number (ℝ)

MISSING 

Distinct6
Distinct (%)< 0.1%
Missing4321
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean4.219758308
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-05-09T16:49:22.112107image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q35
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9868170628
Coefficient of variation (CV)0.2338562995
Kurtosis1.000572041
Mean4.219758308
Median Absolute Deviation (MAD)0
Skewness-1.231357695
Sum804172
Variance0.9738079155
MonotonicityNot monotonic
2024-05-09T16:49:22.155028image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 98142
50.4%
4 51824
26.6%
3 28597
 
14.7%
2 7920
 
4.1%
1 4001
 
2.1%
6 89
 
< 0.1%
(Missing) 4321
 
2.2%
ValueCountFrequency (%)
1 4001
 
2.1%
2 7920
 
4.1%
3 28597
 
14.7%
4 51824
26.6%
5 98142
50.4%
ValueCountFrequency (%)
6 89
 
< 0.1%
5 98142
50.4%
4 51824
26.6%
3 28597
 
14.7%
2 7920
 
4.1%

fitnessAndRecreation
Real number (ℝ)

MISSING 

Distinct6
Distinct (%)< 0.1%
Missing40518
Missing (%)20.8%
Infinite0
Infinite (%)0.0%
Mean3.87354252
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-05-09T16:49:22.195303image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q35
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.085403478
Coefficient of variation (CV)0.2802095168
Kurtosis0.04626159837
Mean3.87354252
Median Absolute Deviation (MAD)1
Skewness-0.7866990554
Sum597982
Variance1.17810071
MonotonicityNot monotonic
2024-05-09T16:49:22.343320image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 54424
27.9%
4 47334
24.3%
3 37486
19.2%
2 8706
 
4.5%
1 6380
 
3.3%
6 46
 
< 0.1%
(Missing) 40518
20.8%
ValueCountFrequency (%)
1 6380
 
3.3%
2 8706
 
4.5%
3 37486
19.2%
4 47334
24.3%
5 54424
27.9%
ValueCountFrequency (%)
6 46
 
< 0.1%
5 54424
27.9%
4 47334
24.3%
3 37486
19.2%
2 8706
 
4.5%

family
Real number (ℝ)

MISSING 

Distinct5
Distinct (%)0.5%
Missing193805
Missing (%)99.4%
Infinite0
Infinite (%)0.0%
Mean3.456382002
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-05-09T16:49:22.383440image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.546514744
Coefficient of variation (CV)0.4474374486
Kurtosis-1.248111281
Mean3.456382002
Median Absolute Deviation (MAD)1
Skewness-0.4992086481
Sum3764
Variance2.391707854
MonotonicityNot monotonic
2024-05-09T16:49:22.428253image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
5 418
 
0.2%
1 224
 
0.1%
3 186
 
0.1%
4 185
 
0.1%
2 76
 
< 0.1%
(Missing) 193805
99.4%
ValueCountFrequency (%)
1 224
0.1%
2 76
 
< 0.1%
3 186
0.1%
4 185
0.1%
5 418
0.2%
ValueCountFrequency (%)
5 418
0.2%
4 185
0.1%
3 186
0.1%
2 76
 
< 0.1%
1 224
0.1%

enrichmentActivities
Real number (ℝ)

MISSING 

Distinct6
Distinct (%)< 0.1%
Missing38758
Missing (%)19.9%
Infinite0
Infinite (%)0.0%
Mean3.598292514
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-05-09T16:49:22.471310image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q35
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.234664159
Coefficient of variation (CV)0.3431250112
Kurtosis-0.6494875768
Mean3.598292514
Median Absolute Deviation (MAD)1
Skewness-0.5362774717
Sum561823
Variance1.524395586
MonotonicityNot monotonic
2024-05-09T16:49:22.513562image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 47031
24.1%
3 40184
20.6%
4 40183
20.6%
2 16501
 
8.5%
1 12208
 
6.3%
6 29
 
< 0.1%
(Missing) 38758
19.9%
ValueCountFrequency (%)
1 12208
 
6.3%
2 16501
 
8.5%
3 40184
20.6%
4 40183
20.6%
5 47031
24.1%
ValueCountFrequency (%)
6 29
 
< 0.1%
5 47031
24.1%
4 40183
20.6%
3 40184
20.6%
2 16501
 
8.5%

service
Real number (ℝ)

MISSING 

Distinct6
Distinct (%)< 0.1%
Missing6307
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean4.224548882
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-05-09T16:49:22.553432image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q35
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.151131538
Coefficient of variation (CV)0.272486263
Kurtosis1.011190007
Mean4.224548882
Median Absolute Deviation (MAD)0
Skewness-1.421468535
Sum796695
Variance1.325103817
MonotonicityNot monotonic
2024-05-09T16:49:22.596022image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 112640
57.8%
4 34441
 
17.7%
3 21278
 
10.9%
2 11224
 
5.8%
1 8915
 
4.6%
6 89
 
< 0.1%
(Missing) 6307
 
3.2%
ValueCountFrequency (%)
1 8915
 
4.6%
2 11224
 
5.8%
3 21278
 
10.9%
4 34441
 
17.7%
5 112640
57.8%
ValueCountFrequency (%)
6 89
 
< 0.1%
5 112640
57.8%
4 34441
 
17.7%
3 21278
 
10.9%
2 11224
 
5.8%

valueForMoney
Real number (ℝ)

MISSING 

Distinct6
Distinct (%)< 0.1%
Missing13117
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean3.82542896
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-05-09T16:49:22.636520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q35
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.259903417
Coefficient of variation (CV)0.329349579
Kurtosis-0.3626843269
Mean3.82542896
Median Absolute Deviation (MAD)1
Skewness-0.8408486299
Sum695375
Variance1.587356621
MonotonicityNot monotonic
2024-05-09T16:49:22.680705image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 73615
37.8%
4 46470
23.8%
3 31480
16.2%
2 16538
 
8.5%
1 13628
 
7.0%
6 46
 
< 0.1%
(Missing) 13117
 
6.7%
ValueCountFrequency (%)
1 13628
 
7.0%
2 16538
 
8.5%
3 31480
16.2%
4 46470
23.8%
5 73615
37.8%
ValueCountFrequency (%)
6 46
 
< 0.1%
5 73615
37.8%
4 46470
23.8%
3 31480
16.2%
2 16538
 
8.5%

embarkation
Real number (ℝ)

MISSING 

Distinct6
Distinct (%)< 0.1%
Missing2781
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean4.219542665
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-05-09T16:49:22.722260image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median5
Q35
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.149658729
Coefficient of variation (CV)0.2724605058
Kurtosis1.233618779
Mean4.219542665
Median Absolute Deviation (MAD)0
Skewness-1.469957297
Sum810629
Variance1.321715192
MonotonicityNot monotonic
2024-05-09T16:49:22.764339image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 112266
57.6%
4 39205
 
20.1%
3 21119
 
10.8%
1 10340
 
5.3%
2 9079
 
4.7%
6 104
 
0.1%
(Missing) 2781
 
1.4%
ValueCountFrequency (%)
1 10340
 
5.3%
2 9079
 
4.7%
3 21119
 
10.8%
4 39205
 
20.1%
5 112266
57.6%
ValueCountFrequency (%)
6 104
 
0.1%
5 112266
57.6%
4 39205
 
20.1%
3 21119
 
10.8%
2 9079
 
4.7%

onboardExperience
Real number (ℝ)

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing99730
Missing (%)51.2%
Infinite0
Infinite (%)0.0%
Mean3.880826783
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-05-09T16:49:22.805216image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q35
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.23783216
Coefficient of variation (CV)0.3189609403
Kurtosis-0.3488582572
Mean3.880826783
Median Absolute Deviation (MAD)1
Skewness-0.8525935587
Sum369315
Variance1.532228456
MonotonicityNot monotonic
2024-05-09T16:49:22.849397image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
5 41325
21.2%
4 21873
 
11.2%
3 17152
 
8.8%
2 8928
 
4.6%
1 5886
 
3.0%
(Missing) 99730
51.2%
ValueCountFrequency (%)
1 5886
 
3.0%
2 8928
 
4.6%
3 17152
8.8%
4 21873
11.2%
5 41325
21.2%
ValueCountFrequency (%)
5 41325
21.2%
4 21873
11.2%
3 17152
8.8%
2 8928
 
4.6%
1 5886
 
3.0%

shoreExcursions
Real number (ℝ)

MISSING 

Distinct6
Distinct (%)< 0.1%
Missing42388
Missing (%)21.7%
Infinite0
Infinite (%)0.0%
Mean3.782861002
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-05-09T16:49:22.894550image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q35
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.174475942
Coefficient of variation (CV)0.3104729308
Kurtosis-0.2524166039
Mean3.782861002
Median Absolute Deviation (MAD)1
Skewness-0.7433625693
Sum576909
Variance1.379393738
MonotonicityNot monotonic
2024-05-09T16:49:22.937482image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 53115
27.3%
4 42951
22.0%
3 35735
18.3%
2 11505
 
5.9%
1 9177
 
4.7%
6 23
 
< 0.1%
(Missing) 42388
21.7%
ValueCountFrequency (%)
1 9177
 
4.7%
2 11505
 
5.9%
3 35735
18.3%
4 42951
22.0%
5 53115
27.3%
ValueCountFrequency (%)
6 23
 
< 0.1%
5 53115
27.3%
4 42951
22.0%
3 35735
18.3%
2 11505
 
5.9%